In [ ]:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pycaret

data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/water_potability.csv")
data.head()
In [2]:
data = data.dropna()
#remove the NAN
data.isnull().sum()
#check the NAN rows
Out[2]:
ph                 0
Hardness           0
Solids             0
Chloramines        0
Sulfate            0
Conductivity       0
Organic_carbon     0
Trihalomethanes    0
Turbidity          0
Potability         0
dtype: int64
In [3]:
plt.figure(figsize=(15, 10))
sns.countplot(data.Potability)
plt.title("Distribution of Unsafe and Safe Water")
plt.show()

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
In [4]:
import plotly.express as px
data = data
figure = px.histogram(data, x = "ph", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: PH")
figure.show()
In [5]:
figure = px.histogram(data, x = "Hardness", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Hardness")
figure.show()
In [6]:
figure = px.histogram(data, x = "Solids", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Solids")
figure.show()
In [7]:
figure = px.histogram(data, x = "Chloramines", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Chloramines")
figure.show()
In [8]:
figure = px.histogram(data, x = "Sulfate", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Sulfate")
figure.show()
In [9]:
figure = px.histogram(data, x = "Conductivity", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Conductivity")
figure.show()
In [10]:
figure = px.histogram(data, x = "Organic_carbon", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Organic Carbon")
figure.show()
In [11]:
figure = px.histogram(data, x = "Trihalomethanes", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Trihalomethanes")
figure.show()
In [12]:
figure = px.histogram(data, x = "Turbidity", 
                      color = "Potability", 
                      title= "Factors Affecting Water Quality: Turbidity")
figure.show()
In [13]:
correlation = data.corr()
correlation["ph"].sort_values(ascending=False)
Out[13]:
ph                 1.000000
Hardness           0.108948
Organic_carbon     0.028375
Trihalomethanes    0.018278
Potability         0.014530
Conductivity       0.014128
Sulfate            0.010524
Chloramines       -0.024768
Turbidity         -0.035849
Solids            -0.087615
Name: ph, dtype: float64
In [ ]:
from pycaret.classification import *
clf = setup(data, target = "Potability", silent = True, session_id = 786)
compare_models()
In [ ]:
!pip install pycaret
!pip install --upgrade pip
In [ ]:
model = create_model("rf")
predict = predict_model(model, data=data)
predict.head()
In [ ]: